OpenRaiser/Intern-Atlas huggingface dataset:
On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars
Venue: arXiv cs.CV 2023
Year: 2023

For driver observation frameworks, clean datasets collected in controlled simulated environments often serve as the initial training ground. Yet, when deployed under real driving conditions, such simulator-trained models quickly face the problem of distributional shifts brought about by changing illumination, car model, variations in subject appearances, sensor discrepancies, and other environmental alterations. This paper investigates the viability of transferring video-based driver observation models from simulation to real-world scenarios in autonomous vehicles, given the frequent use of simulation data in this domain due to safety issues. To achieve this, we record a dataset featuring actual autonomous driving conditions and involving seven participants engaged in highly distracting secondary activities. To enable direct SIM to REAL transfer, our dataset was designed in accordance with an existing large-scale simulator dataset used as the training source. We utilize the Inflated 3D ConvNet (I3D) model, a popular choice for driver observation, with Gradient-weighted Class Activation Mapping (Grad-CAM) for detailed analysis of model decision-making. Though the simulator-based model clearly surpasses the random baseline, its recognition quality diminishes, with average accuracy dropping from 85.7% to 46.6%. We also observe strong variations across different behavior classes. This underscores the challenges of model transferability, facilitating our research of more robust driver observation systems capable of dealing with real driving conditions.
Transferable Attack for Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target models. i.e The conventional attack methods, such as PGD and FGSM, do not transfer well to target models, making it necessary to study the transferable attacks, especially transferable attacks for semantic segmentation. We find two main factors to achieve transferable attack. Firstly, the attack should come with effective data augmentation and translation-invariant features to deal with unseen models. Secondly, stabilized optimization strategies are needed to find the optimal attack direction. Based on the above observations, we propose an ensemble attack for semantic segmentation to achieve more effective attacks with higher transferability. The source code and experimental results are publicly available via our project page: https://github.com/anucvers/TASS.
On the semantics of proofs in classical sequent calculus
Venue: arXiv cs.LO 2023
Year: 2023

We discuss the problem of finding non-trivial invariants of non-deterministic, symmetric cut-reduction procedures in the classical sequent calculus. We come to the conclusion that (an enriched version of) the propositional fragment of GS4 -- i.e. the one-sided variant of Kleene's context-sharing style sequent system G4, where independent rule applications permute freely -- is an ideal framework in which to attack the problem. We show that the graph induced by axiom rules linking dual atom occurrences is preserved under arbitrary rule permutations in the cut-free fragment of GS4. We then refine the notion of axiom-induced graph so as to extend the result to derivations with cuts, and we exploit the invertibility of logical rules to define a global normalisation procedure that preserves the refined axiom-induced graphs, thus yielding a non-trivial invariant of cut-elimination in GS4. Finally, we build upon the result to devise a new proof system for classical propositional logic, where the rule permutations of GS4 reduce to identities.
Towards univalent reference types
Venue: arXiv cs.LO 2023
Year: 2023

We develop a denotational semantics for general reference types in an impredicative version of guarded homotopy type theory, an adaptation of synthetic guarded domain theory to Voevodsky's univalent foundations. We observe for the first time the profound impact of univalence on the denotational semantics of mutable state. Univalence automatically ensures that all computations are invariant under symmetries of the heap -- a bountiful source of program equivalences. In particular, even the most simplistic univalent model enjoys many new equations that do not hold when the same constructions are carried out in the universes of traditional set-level (extensional) type theory.
ChatGPT for Teaching and Learning: An Experience from Data Science Education
Venue: arXiv cs.CY 2023
Year: 2023

ChatGPT, an implementation and application of large language models, has gained significant popularity since its initial release. Researchers have been exploring ways to harness the practical benefits of ChatGPT in real-world scenarios. Educational researchers have investigated its potential in various subjects, e.g., programming, mathematics, finance, clinical decision support, etc. However, there has been limited attention given to its application in data science education. This paper aims to bridge that gap by utilizing ChatGPT in a data science course, gathering perspectives from students, and presenting our experiences and feedback on using ChatGPT for teaching and learning in data science education. The findings not only distinguish data science education from other disciplines but also uncover new opportunities and challenges associated with incorporating ChatGPT into the data science curriculum.
Anticipating Responsibility in Multiagent Planning
Venue: arXiv cs.AI 2023
Year: 2023

Responsibility anticipation is the process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider. The planning setting in this paper includes partial information regarding the initial state and considers formulas in linear temporal logic as positive or negative outcomes to be attained or avoided. We firstly define attribution for notions of active, passive and contributive responsibility, and consider their agentive variants. We then use these to define the notion of responsibility anticipation. We prove that our notions of anticipated responsibility can be used to coordinate agents in a planning setting and give complexity results for our model, discussing equivalence with classical planning. We also present an outline for solving some of our attribution and anticipation problems using PDDL solvers.
The impact of no-code on digital product development
Venue: arXiv cs.SE 2023
Year: 2023

Low- and no-code platforms (LCNC) have become more popular than ever (Kulkarni, 2021), with low-code broadly adopted to optimise internal business processes. Increasingly, startups build their primary software product using no-code platforms (Palios, 2022). This paper explores why entrepreneurs choose no-code platforms to build, launch and scale a software product, what benefits and limitations no-code has, and why they might transition to custom-developed solutions later. Ten semi-structured interviews with successful projects and no-code startup founders were conducted. The results show that speed, cost savings and the lack of coding knowledge are the primary reasons entrepreneurs choose no-code initially. Challenges are diverse and depend on the no-code platform, the maker's skill and the product. The impact of no-code on established product development/product management frameworks and the maker's role are discussed.
Numerical Modeling of Stress Corrosion Cracking in Steel Structures with Phase Field Method
Venue: arXiv cs.CE 2023
Year: 2023

This study presents a novel coupled mechano-electro-chemical formulation for predicting stress corrosion cracking (SCC) phenomena in steel structures using the phase field method. SCC is a complex damage process that arises from the interaction between mechanical loading and corrosion in a corrosive electrolyte environment. The proposed formulation introduces a new phase-field parameter that aggregates the damage due to mechanical loading and electro-chemical corrosion. To achieve this goal, the internal energies governing the SCC phenomenon are separated into elastic-damage strain energy, the interfacial reaction energy, and energy resulting from changes in corrosion ion concentration. The Allen-Cahn equation is modified to include all energy contributions and calculate the phase field parameter. Furthermore, a specific interfacial kinetic coefficient is introduced to the mechanical energy to take into account corrosion current effects on mechanical properties. The Cahn-Hilliard equation is applied to model the corrosion ion concentration in the domain and the mechanical state of the body is obtained by solving the equilibrium equations. Several numerical examples are presented to validate the robustness and accuracy of the proposed formulation. Finally, the method is applied to predict crack propagation resulting from SCC on two practical engineering problems, yielding promising results.
Advancing Smart Malnutrition Monitoring: A Multi-Modal Learning Approach for Vital Health Parameter Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning. Periodic examinations and mass screenings, incorporating both conventional and non-invasive techniques, have been employed to combat this challenge. However, these approaches suffer from critical limitations, such as the need for additional equipment, lack of comprehensive feature representation, absence of suitable health indicators, and the unavailability of smartphone implementations for precise estimations of Body Fat Percentage (BFP), Basal Metabolic Rate (BMR), and Body Mass Index (BMI) to enable efficient smart-malnutrition monitoring. To address these constraints, this study presents a groundbreaking, scalable, and robust smart malnutrition-monitoring system that leverages a single full-body image of an individual to estimate height, weight, and other crucial health parameters within a multi-modal learning framework. Our proposed methodology involves the reconstruction of a highly precise 3D point cloud, from which 512-dimensional feature embeddings are extracted using a headless-3D classification network. Concurrently, facial and body embeddings are also extracted, and through the application of learnable parameters, these features are then utilized to estimate weight accurately. Furthermore, essential health metrics, including BMR, BFP, and BMI, are computed to conduct a comprehensive analysis of the subject's health, subsequently facilitating the provision of personalized nutrition plans. While being robust to a wide range of lighting conditions across multiple devices, our model achieves a low Mean Absolute Error (MAE) of $\pm$ 4.7 cm and $\pm$ 5.3 kg in estimating height and weight.
Iterated Resultants in CAD
Venue: arXiv cs.SC 2023
Year: 2023

Cylindrical Algebraic Decomposition (CAD) by projection and lifting requires many iterated univariate resultants. It has been observed that these often factor, but to date this has not been used to optimise implementations of CAD. We continue the investigation into such factorisations, writing in the specific context of SC-Square.
On the Complexity of Algorithms with Predictions for Dynamic Graph Problems
Venue: arXiv cs.DS 2023
Year: 2023

{\em Algorithms with predictions} incorporate machine learning predictions into algorithm design. A plethora of recent works incorporated predictions to improve on worst-case optimal bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike in online algorithms, the main goal in dynamic data structures is to maintain the solution {\em efficiently} with every update. Motivated by work in online algorithms, we investigate three natural models of predictions: (1) $\varepsilon$-accurate predictions where each predicted request matches the true request with probability at least $\varepsilon$, (2) list-accurate predictions where a true request comes from a list of possible requests, and (3) bounded delay predictions where the true requests are some (unknown) permutations of the predicted requests. For $\varepsilon$-accurate predictions, we show that lower bounds from the non-prediction setting of a problem carry over, up to a $1-\varepsilon$ factor. Then we give general reductions among the prediction models for a problem, showing that lower bounds for bounded delay imply lower bounds for list-accurate predictions, which imply lower bounds for $\varepsilon$-accurate predictions. Further, we identify two broad problem classes based on lower bounds due to the Online Matrix Vector (OMv) conjecture. Specifically, we show that dynamic problems that are {\em locally correctable} have strong conditional lower bounds for list-accurate predictions that are equivalent to the non-prediction setting, unless list-accurate predictions are perfect. Moreover, dynamic problems that are {\em locally reducible} have a smooth transition in the running time. We categorize problems accordingly and give upper bounds that show that our lower bounds are almost tight, including problems in dynamic graphs.
Structural Transfer Learning in NL-to-Bash Semantic Parsers
Venue: arXiv cs.CL 2023
Year: 2023

Large-scale pre-training has made progress in many fields of natural language processing, though little is understood about the design of pre-training datasets. We propose a methodology for obtaining a quantitative understanding of structural overlap between machine translation tasks. We apply our methodology to the natural language to Bash semantic parsing task (NLBash) and show that it is largely reducible to lexical alignment. We also find that there is strong structural overlap between NLBash and natural language to SQL. Additionally, we perform a study varying compute expended during pre-training on the English to German machine translation task and find that more compute expended during pre-training does not always correspond semantic representations with stronger transfer to NLBash.
The flow of ideas in word embeddings
Venue: arXiv cs.CL 2023
Year: 2023

The flow of ideas has been extensively studied by physicists, psychologists, and machine learning engineers. This paper adopts specific tools from microrheology to investigate the similarity-based flow of ideas. We introduce a random walker in word embeddings and study its behavior. Such similarity-mediated random walks through the embedding space show signatures of anomalous diffusion commonly observed in complex structured systems such as biological cells and complex fluids. The paper concludes by proposing the application of popular tools employed in the study of random walks and diffusion of particles under Brownian motion to assess quantitatively the incorporation of diverse ideas in a document. Overall, this paper presents a self-referenced method combining microrheology and machine learning concepts to explore the meandering tendencies of language models and their potential association with creativity.
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Venue: arXiv cs.LG 2023
Year: 2023

The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretability, and fairness. The last decade saw the development of numerous methods addressing these challenges. In this survey, we systematically review these advancements from a data-centric perspective, highlighting the shortcomings of traditional empirical risk minimization (ERM) training in handling challenges posed by the data. Interestingly, we observe a convergence of these methods, despite being developed independently across trustworthy machine learning subfields. Pearl's hierarchy of causality offers a unifying framework for these techniques. Accordingly, this survey presents the background of trustworthy machine learning development using a unified set of concepts, connects this language to Pearl's causal hierarchy, and finally discusses methods explicitly inspired by causality literature. We provide a unified language with mathematical vocabulary to link these methods across robustness, adversarial robustness, interpretability, and fairness, fostering a more cohesive understanding of the field. Further, we explore the trustworthiness of large pretrained models. After summarizing dominant techniques like fine-tuning, parameter-efficient fine-tuning, prompting, and reinforcement learning with human feedback, we draw connections between them and the standard ERM. This connection allows us to build upon the principled understanding of trustworthy methods, extending it to these new techniques in large pretrained models, paving the way for future methods. Existing methods under this perspective are also reviewed. Lastly, we offer a brief summary of the applications of these methods and discuss potential future aspects related to our survey. For more information, please visit http://trustai.one.
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi.
A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations
Venue: arXiv cs.CE 2023
Year: 2023

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior.
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model
Venue: arXiv cs.LG 2023
Year: 2023

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of why one instance is an anomaly? and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and compared. The overall accuracy and F score of OPT_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.
AirTouch: Towards Safe Human-Robot Interaction Using Air Pressure Feedback and IR Mocap System
Venue: arXiv cs.RO 2023
Year: 2023

The growing use of robots in urban environments has raised concerns about potential safety hazards, especially in public spaces where humans and robots may interact. In this paper, we present a system for safe human-robot interaction that combines an infrared (IR) camera with a wearable marker and airflow potential field. IR cameras enable real-time detection and tracking of humans in challenging environments, while controlled airflow creates a physical barrier that guides humans away from dangerous proximity to robots without the need for wearable devices. A preliminary experiment was conducted to measure the accuracy of the perception of safety barriers rendered by controlled air pressure. In a second experiment, we evaluated our approach in an imitation scenario of an interaction between an inattentive person and an autonomous robotic system. Experimental results show that the proposed system significantly improves a participant's ability to maintain a safe distance from the operating robot compared to trials without the system.
Entropy Maximization in Sparse Matrix by Vector Multiplication ($\max_E SpMV$)
Venue: arXiv cs.DC 2023
Year: 2023

The peak performance of any SpMV depends primarily on the available memory bandwidth and its effective use. GPUs, ASICs, and new FPGAs have higher and higher bandwidth; however, for large scale and highly sparse matrices, SpMV is still a hard problem because of its random access pattern and workload imbalance. Here, we show how to turn randomness to our advantage. We propose a matrix permutation pre-processing step that aims to maximize the entropy of the distribution of the nonzero elements. We seek any permutation that uniformly distributes the non-zero elements' distribution, thereby generating a SpMV problem that is amenable to work load balancing or to speed up sort algorithms. We conjecture these permutations would be most effective for matrices with no dense rows or columns and, as in preconditioning, when the matrix is reused. We shall show that entropy maximization is an optimization that any architecture may take advantage although in different ways. Most importantly, any developer can consider and deploy. We shall present cases where we can improve performance by 15\% on AMD-based (GPU-CPU) systems.